For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost-deployment balance. However, such spatial sparsity of EEG limits spatial fidelity, hurting learning and introducing bias. EEG spatial super-resolution methods aim to recover high-density EEG signals from sparse measurements, yet is often hindered by distribution shift and signal distortion and thus reducing fidelity and usability for EEG analysis and visualization. To overcome these challenges, we introduce SRGDiff, a step-aware residual-guided diffusion model that formulates EEG spatial super-resolution as dynamic conditional generation. Our key idea is to learn a dynamic residual condition from the low-density input that predicts the step-wise temporal and spatial details to add and uses the evolving cue to steer the denoising process toward high density reconstructions. At each denoising step, the proposed residual condition is additively fused with the previous denoiser feature maps, then a step-dependent affine modulation scales and shifts the activation to produce the current features. This iterative procedure dynamically extracts step-wise temporal rhythms and spatial-topographic cues to steer high-density recovery and maintain a fidelity-consistency balance. We adopt a comprehensive evaluation protocol spanning signal-, feature-, and downstream-level metrics across SEED, SEED-IV, and Localize-MI and multiple upsampling scales. SRGDiff achieves consistent gains of up to 40% over strong baselines, proving its superiority in the task of EEG spatial super-resolution. Moreover, topographic visualizations comparison and substantial EEG-FID gains jointly indicate that our SR EEG mitigates the spatial-spectral shift between low- and high-density recordings. Our code is available at https://github.com/DhrLhj/ICLR2026SRGDiff.

翻译：在实际脑机接口应用中，轻量化脑电图系统提供了最佳的部署成本平衡。然而，这种脑电信号的空间稀疏性限制了空间保真度，损害了学习性能并引入了偏差。脑电空间超分辨率方法旨在从稀疏测量中恢复高密度脑电信号，但常受分布偏移和信号失真的阻碍，从而降低了脑电分析与可视化的保真度和可用性。为克服这些挑战，我们提出了SRGDiff，一种步感知残差引导扩散模型，将脑电空间超分辨率表述为动态条件生成。我们的核心思想是从低密度输入中学习动态残差条件，该条件预测需添加的逐步时空细节，并利用这一演化线索引导去噪过程朝向高密度重建。在每个去噪步骤中，所提出的残差条件与先前的去噪器特征图进行加性融合，随后通过步依赖的仿射调制对激活进行缩放和偏移以生成当前特征。这一迭代过程动态提取逐步时间节律和空间地形线索，以引导高密度恢复并保持保真度与一致性的平衡。我们采用了一套涵盖信号层、特征层及下游任务指标的全面评估协议，在SEED、SEED-IV和Localize-MI数据集及多种上采样尺度上进行测试。SRGDiff在强基线模型上实现了高达40%的稳定性能提升，证明了其在脑电空间超分辨率任务中的优越性。此外，地形可视化对比与显著的EEG-FID增益共同表明，我们的超分辨率脑电有效缓解了低密度与高密度记录间的空间-频谱偏移。代码已开源：https://github.com/DhrLhj/ICLR2026SRGDiff。